Tag: embedding
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Contrast Creates Meaning
Labels aren’t necessary. ImageNet needed 25,000 workers to label 14 million images. But the internet already has the answers—400 million image-text pairs. CLIP learned without labels and classifies things it’s never seen. How contrastive learning aligned images and text into one space.
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Into a Shared Space
2012. CNN conquered images. Transformer conquered text. But each lived in separate worlds—vectors that couldn’t compare. What if a cat photo and the word “cat” existed at the same location? Shared embedding space makes this possible. How CLIP and ImageBind unified different senses into one language.